Research on Level Effect-Based Fuzzy Prediction Method

2014 ◽  
Vol 519-520 ◽  
pp. 780-783
Author(s):  
Fachao Li ◽  
Shuo Liu
Keyword(s):  
Structural Characteristics ◽  
Prediction Method ◽  
Practical Significance ◽  
Fuzzy Decision Making ◽  
Effect Function ◽  
Fuzzy Environment ◽  
Optimal Value ◽  
Measure Model ◽  
Prediction Problems ◽  
Level Effect

As there are many prediction problems under fuzzy environments, describing the prediction results systematically and constructing a fuzzy prediction method with good structural characteristics have attracted an extensive attention. For the prediction of investment return under fuzzy environment, we first make an analysis of general fuzzy decision-making problem, and point out its limitations. Then, we discuss the association feature between decision and membership state, and give a level effect function which can describe the recognized degree under different level cut sets. Furthermore, we establish a measure model for fuzzy optimal value based on level effect function. Finally, we apply the established model to a concrete investment example, and analyze its effectiveness in fuzzy prediction. Theoretical analysis and case study show that this method has good structural characteristics and practical significance, it can enrich the existing fuzzy prediction methods to a certain degree.

Fuzzy Roughness Degree Measurement Model Based on Level Effect

2014 ◽  
Vol 610 ◽  
pp. 377-380
Author(s):  
Fa Chao Li ◽  
Jian Ning Yin
Keyword(s):  
Structural Characteristics ◽  
Measurement Model ◽  
Uncertain Information ◽  
Practical Applications ◽  
Effect Function ◽  
Model Based ◽  
Decision Making System ◽  
Sets Theory ◽  
Applied Fields ◽  
Level Effect

Nowadays there are two kinds of tools which are widely used in uncertain information processing, namely, the fuzzy sets theory and fuzzy roughness sets theory. Besides, how to construct a fusion method for the two kinds of uncertain information systematically has been a focus in both academic and applied fields. In this paper, we put forward the concept of level effect function, and analyze the characteristics of a kind of level effect function. Furthermore, we present the fuzzy roughness degree measurement model based on level effect (FRD-BLE), by combining with the roughness measurement method of level cut set. All the results indicate that FRD-BLE has good structural characteristics and interpretability both in theoretical analysis and practical applications. It can be easily apply the fuzzy processing consciousness into decision-making system with the aid help of FRD-BLE.

A TODIM-Based Decision Support Framework for G-Resilient Supplier Selection in Fuzzy Environment

2016 ◽  
Vol 33 (05) ◽  
pp. 1650033 ◽  
Author(s):  
Dilip Kumar Sen ◽  
Saurav Datta ◽  
Siba Sankar Mahapatra
Keyword(s):  
Decision Support ◽  
Supplier Selection ◽  
Evaluation Criteria ◽  
Fuzzy Topsis ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
Selection Problems ◽  
Fuzzy Environment ◽  
Decision Support Framework ◽  
Application Potential

A novel decision support framework has been proposed herein to solve supplier selection problems by considering green as well as resiliency criteria, simultaneously. In this work subjectivity of evaluation criteria has been tackled by exploring fuzzy set theory. A dominance based approach has been conceptualized which is basically a simplified version of TODIM. Application potential of the proposed dominance based fuzzy decision making approach has been compared to that of fuzzy-TOPSIS, fuzzy-VIKOR and also fuzzy-TODIM. The concept of a unique performance index, i.e. “g-resilient” index has been introduced here to help in assessing suppliers’ performance and thereby selecting the best candidate. The work has also been extended to identify the areas in which suppliers are lagging; these seek further improvement towards g-resilient suppliers’ performance to be boosted up to the desired level.

Bioinspired Algorithms in Solving Three-Dimensional Protein Structure Prediction Problems

2017 ◽  
pp. 316-337
Author(s):  
Raghunath Satpathy
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
3D Structure ◽  
Prediction Method ◽  
Optimization Methods ◽  
Point Of View ◽  
Living Organisms ◽  
Prediction Problems

Proteins play a vital molecular role in all living organisms. Experimentally, it is difficult to predict the protein structure, however alternatively theoretical prediction method holds good for it. The 3D structure prediction of proteins is very much important in biology and this leads to the discovery of different useful drugs, enzymes, and currently this is considered as an important research domain. The prediction of proteins is related to identification of its tertiary structure. From the computational point of view, different models (protein representations) have been developed along with certain efficient optimization methods to predict the protein structure. The bio-inspired computation is used mostly for optimization process during solving protein structure. These algorithms now a days has received great interests and attention in the literature. This chapter aim basically for discussing the key features of recently developed five different types of bio-inspired computational algorithms, applied in protein structure prediction problems.

Research on O2O Platform and Promotion Algorithm of Sports Venues Based on Deep Learning Technique

2018 ◽  
Vol 13 (3) ◽  
pp. 73-84
Author(s):  
Kaiyan Han ◽  
Qin Wang
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Joint Distribution ◽  
Big Data Analysis ◽  
Practical Significance ◽  
Hidden Variable ◽  
Optimal Value ◽  
Learning Technique ◽  
Sports Venues

In the era of big data, intelligent sports venues have a practical significance to provide personalized service for users and build up a platform for stadium management. This article proposes a new parallel big data promotion algorithm based on the latest achievements of big data analysis. The proposed algorithm calculates the optimal value by using the observed variables Y, the hidden variable data Z, the joint distribution P (Y, Z | θ) and distribution conditions P (Z | Y | θ). The experimental results show that the proposed algorithm has higher accuracy of big data analysis, and can serve the intelligent sports venues better.

TOPTMH: TOPOLOGY PREDICTOR FOR TRANSMEMBRANE α-HELICES

2010 ◽  
Vol 08 (01) ◽  
pp. 39-57 ◽  
Author(s):  
REZWAN AHMED ◽  
HUZEFA RANGWALA ◽  
GEORGE KARYPIS
Keyword(s):  
Markov Models ◽  
Structural Characteristics ◽  
Transmembrane Helix ◽  
3D Structure ◽  
Prediction Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Sequence Information ◽  
Vector Machines ◽  
Prediction Approach

Alpha-helical transmembrane proteins mediate many key biological processes and represent 20%–30% of all genes in many organisms. Due to the difficulties in experimentally determining their high-resolution 3D structure, computational methods to predict the location and orientation of transmembrane helix segments using sequence information are essential. We present TOPTMH, a new transmembrane helix topology prediction method that combines support vector machines, hidden Markov models, and a widely used rule-based scheme. The contribution of this work is the development of a prediction approach that first uses a binary SVM classifier to predict the helix residues and then it employs a pair of HMM models that incorporate the SVM predictions and hydropathy-based features to identify the entire transmembrane helix segments by capturing the structural characteristics of these proteins. TOPTMH outperforms state-of-the-art prediction methods and achieves the best performance on an independent static benchmark.

scholarly journals Research on the Computational Prediction of Essential Genes

2021 ◽  
Vol 9 ◽  
Author(s):  
Yuxin Guo ◽  
Ying Ju ◽  
Dong Chen ◽  
Lihong Wang
Keyword(s):  
Genetic Testing ◽  
Essential Gene ◽  
Prediction Method ◽  
Computational Prediction ◽  
Essential Genes ◽  
Blood Type ◽  
Practical Significance ◽  
Biological Traits ◽  
Genome Database ◽  
Wide Range

Genes, the nucleotide sequences that encode a polypeptide chain or functional RNA, are the basic genetic unit controlling biological traits. They are the guarantee of the basic structures and functions in organisms, and they store information related to biological factors and processes such as blood type, gestation, growth, and apoptosis. The environment and genetics jointly affect important physiological processes such as reproduction, cell division, and protein synthesis. Genes are related to a wide range of phenomena including growth, decline, illness, aging, and death. During the evolution of organisms, there is a class of genes that exist in a conserved form in multiple species. These genes are often located on the dominant strand of DNA and tend to have higher expression levels. The protein encoded by it usually either performs very important functions or is responsible for maintaining and repairing these essential functions. Such genes are called persistent genes. Among them, the irreplaceable part of the body’s life activities is the essential gene. For example, when starch is the only source of energy, the genes related to starch digestion are essential genes. Without them, the organism will die because it cannot obtain enough energy to maintain basic functions. The function of the proteins encoded by these genes is thought to be fundamental to life. Nowadays, DNA can be extracted from blood, saliva, or tissue cells for genetic testing, and detailed genetic information can be obtained using the most advanced scientific instruments and technologies. The information gained from genetic testing is useful to assess the potential risks of disease, and to help determine the prognosis and development of diseases. Such information is also useful for developing personalized medication and providing targeted health guidance to improve the quality of life. Therefore, it is of great theoretical and practical significance to identify important and essential genes. In this paper, the research status of essential genes and the essential genome database of bacteria are reviewed, the computational prediction method of essential genes based on communication coding theory is expounded, and the significance and practical application value of essential genes are discussed.

scholarly journals A computational method for predicting nucleocapsid protein in retroviruses

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Manyun Guo ◽  
Yucheng Ma ◽  
Wanyuan Liu ◽  
Zuyi Yuan
Keyword(s):  
Nucleocapsid Protein ◽  
Gene Prediction ◽  
Specific Antigen ◽  
Prediction Method ◽  
Computational Method ◽  
Functional Study ◽  
Support Vector ◽  
Length Variation ◽  
Wide Range ◽  
Prediction Problems

AbstractNucleocapsid protein (NC) in the group-specific antigen (gag) of retrovirus is essential in the interactions of most retroviral gag proteins with RNAs. Computational method to predict NCs would benefit subsequent structure analysis and functional study on them. However, no computational method to predict the exact locations of NCs in retroviruses has been proposed yet. The wide range of length variation of NCs also increases the difficulties. In this paper, a computational method to identify NCs in retroviruses is proposed. All available retrovirus sequences with NC annotations were collected from NCBI. Models based on random forest (RF) and weighted support vector machine (WSVM) were built to predict initiation and termination sites of NCs. Factor analysis scales of generalized amino acid information along with position weight matrix were utilized to generate the feature space. Homology based gene prediction methods were also compared and integrated to bring out better predicting performance. Candidate initiation and termination sites predicted were then combined and screened according to their intervals, decision values and alignment scores. All available gag sequences without NC annotations were scanned with the model to detect putative NCs. Geometric means of sensitivity and specificity generated from prediction of initiation and termination sites under fivefold cross-validation are 0.9900 and 0.9548 respectively. 90.91% of all the collected retrovirus sequences with NC annotations could be predicted totally correct by the model combining WSVM, RF and simple alignment. The composite model performs better than the simplex ones. 235 putative NCs in unannotated gags were detected by the model. Our prediction method performs well on NC recognition and could also be expanded to solve other gene prediction problems, especially those whose training samples have large length variations.

scholarly journals The method of parameter reassessment for prediction of NCP(2019-nCoV) spread

2020 ◽  
Author(s):  
Jin-Li Guo ◽  
Ya-Zhi Fu
Keyword(s):  
Conversion Rate ◽  
Estimation Method ◽  
Markov Property ◽  
Prediction Method ◽  
Control Measures ◽  
Cumulative Number ◽  
Logistic Curve ◽  
Rate Prediction ◽  
Novel Coronavirus ◽  
Prediction Problems

Abstract This paper proposes a conversion rate prediction method and a parameter reevaluation method based on Logistic curve (S-curve) to predict the spread of NCP (the Novel coronavirus pneumonia). According to the statistical data, we use the conversion rate prediction method to predict the spread of NCP. The prediction accuracy is quite high. By fitting the cumulative number of NCP sufferers with the logistic curve, the average estimation method of the limit number is proposed to predict the spread of NCP and the limit number of sufferers. This paper also assessing the effectiveness of prevention and control measures with the dynamic estimation of the infection probability of NCP. Based on the Markov property, the parameter reevaluation method proposed in this paper avoids over-fitting the theoretical curve and improves the accuracy of prediction. This research idea is not only suitable for Logistic curve regression, but also for other regression prediction problems.

Sign in / Sign up

Export Citation Format

Share Document